The Role:

We are looking for an enthusiastic Data Scientist to join our growing team. The hire will

be responsible for working in collaboration with other data scientists and engineers

across the organization to develop production-quality models for a variety of problems

across Razorpay. Some possible problems include : making recommendations to

merchants from Razorpay’s suite of products, cost optimization of transactions for

merchants, automatic address disambiguation / correction to enable tracking customer

purchases using advanced natural language processing techniques.

As part of the DS team @ Razorpay, you’ll work with some of the smartest

engineers/architects/data scientists in the industry and have the opportunity to solve

complex and critical problems for Razorpay.

You come and work with the right attitude, fun and growth guaranteed!

Roles & Responsibilities:

1. Solve business problems by applying data science and machine learning.
2. Collaborate with cross-functional teams to build and deploy data science solutions.
3. Analyze large volumes of data to generate actionable insights.
4. Present findings and recommendations to stakeholders.
5. Identify key metrics, conduct exploratory data analysis, and create executive dashboards.
6. Support multiple projects in a fast-paced environment.
7. Train and maintain machine learning models.
8. Continuously improve solutions and evaluate their effectiveness.
9. Deploy data-driven solutions and communicate results effectively.

Mandatory Qualifications

1. 1-2 years of experience doing ML and building ML models
2. Bachelors (required) or Masters in a quantitative field such as Computer science, operations research, statistics, mathematics, physics
3. Sound knowledge of basic machine learning techniques : regression, classification, clustering, model metrics and performance (AUC, ROC, precision, recall and their various flavours)
4. Experienced in coding in python and good knowledge of at least one language from C, C++, Java
5. Familiarity with one or more scripting languages : perl, command-line Unix
6. Interest in learning and using deep learning frameworks such as Tensorflow, Keras, pytorch and big data tools like Spark and databricks / AWS / GCP / Microsoft Azure to write production quality ML code
7. An interest in ML experimentation end-to-end : how to experiment with models, how to report success, A/B testing.
8. Good communication skills and ability to keep stakeholders informed of progress / blockers